Surface weather observations are very important for local and regional (meso- beta and gamma) scale weather analysis and 0 - 12hr forecast. In fact, many weather-critical businesses, such as the Army test ranges, deploy and maintain surface weather station network to support their applications. Effectively assimilation of surface observations into high-resolution mesoscale models has also been one of the high-interest scientific topics for improving regional and local-scale weather analysis and prediction. Recently, NCAR RTFDDA (real-time four-dimensional data assimilation and forecasting) system, a Newtonian relaxation “observation nudging” based operational mesoscale scale forecasting capability that has been developed to provide weather support to the test and evaluation activities at eight US Army ranges, was enhanced with a four-dimensional relaxation ensemble Kalman filter (4D-REKF) FDDA technology. 4D-REKF is an ensemble data assimilation and ensemble forecasting system and the 4D-REKF data assimilation scheme permits to use flow-dependent background error covariance to spread the observation information in the state space of the WRF model. In this study, 4D-REKF is adapted to use climatological weather-regime based spatial correlation (Kc) in the place of ensemble-based Kalman gain (Ke) to improve the surface data assimilation. With this approach, the weather day/times that are similar to the weather regime of the current weather are identified from the recent and/or historical high-resolution model forecasts, and these forecasts are collected and form a “climate” ensemble. Kalman gains are computed with this ensemble (so-called “climate-ensemble Kalman gains”: Kc) and ingested in 4D-REKF to enhance the surface data assimilation in WRF. Case studies have been conducted to test this algorithm and the result shows obvious improvements from RTFDDA which is based on the Cressman-type weight function for surface data assimilation.